class_docstr: An implementation of `"FGSD" <https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs>`_
  from the NeurIPS '17 paper "Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs".
  The procedure calculates the Moore-Penrose spectrum of the normalized Laplacian.
  Using this spectrum the histogram of the spectral features is used as a whole graph representation.
  
  Args:
      hist_bins (int): Number of histogram bins. Default is 200.
      hist_range (int): Histogram range considered. Default is 20.
      seed (int): Random seed value. Default is 42.
code: def __init__(
      self, hist_bins: int = 200, hist_range: int = 20, seed: int = 42
  ):
      self.hist_bins = hist_bins
      self.hist_range = (0, hist_range)
      self.seed = seed
  
file_path: karateclub/karateclub/graph_embedding/fgsd.py
imports: import numpy
  import networkx
  from typing import List
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new FGSD object from inherited base classes: Estimator with example: obj = FGSD(hist_bins, hist_range, seed)